Coronary artery disease (CAD) is a major cause of death in industrialized nations, and approximately 13 million people in the United States are estimated to have the disease. CAD is caused by the thickening and hardening of arterial walls, as well as plaque deposits (including fat, cholesterol, fibers, calcium, and other substances from the blood) accumulated in the arteries. Over time, the plaque deposits narrow the arteries and deprive the heart of oxygen. This can cause blood clots, and in some instances, can completely block arteries, causing blood flow to the heart to stop. Reduced blood flow reduces the oxygen supply to the heart muscles, which can cause chest pain (angina), heart attack, heart failure, or arrhythmias. Often, sudden death results. Thus, there is an urgent need for a non-invasive way to detect and screen for coronary occlusions so that simple, inexpensive treatment plans (including diet and/or drugs) can be expeditiously implemented to reverse the disease before it damages the heart tissue.
To date, the only definitive test for CAD is coronary angiography, a procedure which is invasive, expensive, requires hospitalization, and carries health risks. Newer technologies, such as electron beam Computer Tomography (ebCT), expose the patient to possible health risks from radiation and/or dye contrast agents, are very costly, and require major capital investments and specialized operational staff. Older technologies, such as stress electrocardiology (ECG), expose the patient to moderate risk, remain labor intensive, are still fairly expensive, and have uncomfortably low specificity and sensitivity, especially for women.
In the past, various techniques have been developed for determining the presence of CAD in a patient through analysis of acoustic heart signals taken at one or more locations near the patient's heart. Unfortunately, such techniques analyzed only a very limited range of feature parameters associated with the acoustic signal, and often analyze only a limited range of frequencies of the acoustic signal. Moreover, the presence of noise in the acoustic signal can significantly adversely affect the ability of existing techniques to accurately diagnose CAD in a patient. Finally, clinical studies have shown only modest specificity.
Accordingly, what would be desirable, but has not yet been provided, is a system and method for acoustic detection of coronary heart disease, which address the foregoing limitations of existing detection techniques.